Dataset: Microplastic addition has no detectable effect on ecosystem metabolism or diel dinitrogen flux in a large in-lake mesocosm experiment under oligotrophic conditions
Bibliographic record
Abstract
This dataset and R code are relevant to the following: Data location and timeframe: All data were collected in 2021 in Lake 378 at the Experimental Lakes Area in Ontario, Canada Study Title: Microplastic addition has no detectable effect on ecosystem metabolism or diel dinitrogen flux in a large in-lake mesocosm experiment under oligotrophic conditions Authors: Raul Lazcano et al. Overarching project: The "pELAstic" project: A series of experimental additions of microplastics to Lake 378 at ELA, including pelagic mesocosms, littoral mesocosms, and the whole lake. Organization: The published datasets and code are divided into 5 folders. Folders 1 and 2 have the data and code to calculate ecosystem metabolism in the mesocosms and at the whole-lake scale Folder 3 has the data and code to calculate N2 flux in the mesocosms and whole-lake scale Folder 3 also has the data and code to compare the N2 dynamics across mescocosms and between lake habitats (epilimnion and hypolimnion) Folder 4 has the data and code for generalized linear mixed models (GLMM) to compare dissolved oyxgen, temperature, and metabolsim among mesocosms Folder 5 has the data and code for GLMM to compare metabolism and N2 dynamics between the mesocosms and the whole-lake level. Additional context: For the R code files in each folder, the code begins with a heading describing the contents Within the code, directions are noted throughout to describe steps for figure composition and statistical analysis To generate the summary stats tables in the manuscript and supplemental information The output from Rstudio was pasted into excel, and the table formatting completed within excel
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.045 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".